Even though the acronym AI is now well-embedded in banking industry consciousness, actual use of artificial intelligence, and its cousins machine learning and data analytics, has been limited except with a handful of the largest financial institutions.
According to an MIT Sloan report cited by IBM, 81% of all enterprises do not understand what data is required for AI, or how to access it. Still, the report found that 83% agree that driving AI across the enterprise is a strategic opportunity.
Meanwhile the big tech firms, notably Amazon, Google and Facebook, have built big leads in this area, powering their ecommerce empires, which increasingly include financial services.
There are options, however, to enable a wider range of financial institutions to take advantage of AI for use in marketing, personalization, user experience, payments, and more.
The Financial Brand caught up with Vikram Bhalchandra of Clairvoyant, to discuss some of these options. Bhalchandra, Sales and Marketing Head for the big data firm, has more than 22 years experience including time at Capgemini, UST Global and Reliance Group, in addition to Clairvoyant. He addresses AI’s potential to empower financial institution marketing and digital commerce.
How can financial institutions leverage AI for marketing credit cards and other retail financial products?
Vikram Bhalchandra: The standard practice of sending out the same offer to every consumer is neither efficient nor effective. As a result, direct marketing response rates generally are less than 2% for credit card offers. Where artificial intelligence and machine learning come into play to increase that response rate is through greater consumer segmentation and targeting.
There are vast amounts of data available today that financial institutions can use to increase the effectiveness of targeting, so that the right offer can be pitched to the right user (e.g. a card with travel rewards to someone who travels frequently). This data can come from a consumer’s online and offline behavior, past purchase history, third-party data that is available through credit bureaus, and other publicly available records.
The more data used, the better the targeting becomes. This is where AI-powered engines and algorithms can be used to process all of this data, learn from the insights, and then make the same kind of decisions or recommendations that a human would make — except at scale and with billions of real-time data points for millions of consumers.
Some bank and credit union marketers are already beginning to integrate non-traditional data sources like social media to better target and personalize messages and offers. Having this kind of additional lifestyle data can not only help financial institutions better cross-sell and up-sell different product options, but eventually get to the point where the institution becomes almost a personalized shopping assistant, helping consumers make purchase decisions by suggesting, for example, the best credit card to use to get the best deals and rewards. This type of innovation will have a major impact on marketing ROI.
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How can AI be used specifically to develop the most effective rewards programs?
VB: Rewards programs are designed primarily as an incentive for consumers to use a card or other product more often. It also creates “stickiness,” reduces churn and boosts loyalty. The status quo for many rewards programs is a generic offering — same points, same things you can redeem points for as the competition. A more effective rewards program is one that is much more customized to individual consumers based on their interests.
AI and advanced analytics can be used to create highly individualized rewards programs automatically and in real time, in the same way that they are being used by Amazon and others to recommend products online.
In fact, rewards programs don’t even have to be points based. They can include things like exclusive first-access to discounts on concert tickets, based on a real-time understanding of consumers’ tastes in music, what concerts they’ve bought tickets to in the past and other data.
How can financial institutions use AI to tie themselves more closely to ecommerce merchants?
VB: Even though Amazon, Walmart and eBay hold a lot of power over the ecommerce landscape, there are still many smaller, independent merchants popping up all the time all over the world. This presents opportunities for tie-ins between financial institutions and online merchants.
If I am a small merchant — or an up and coming direct-to-consumer (D2C) brand selling online — I need to provide customers with secure payment options like PayPal or a credit card that can be trusted. Similarly, card issuers that want to expand their reach globally need to know what merchants to approach, and what kind of products to offer. AI allows financial institutions to finely segment the marketplace — both consumers and merchants — and therefore provide customized partnerships with those merchants in a way that benefits both parties through increased digital sales.
Related to this, design thinking needs to be a critical part of any payment process to help reduce friction for the end user. Any delays or cumbersome forms to fill out will negatively affect the user experience, and in many cases lead to cart abandonment. Even with PayPal, shoppers still need to leave the merchant site, log into PayPal, and then return to the merchant site to complete checkout. Card issuing banks and credit unions have a big opportunity to make the payment process more seamless.
Do you see evidence of increasing collaboration within banking to tap into AI solutions?
VB: Yes. Many smaller banks and credit unions do not have the core capabilities or budget to create AI solutions internally, nor do they have to. The reality is that AI solutions will in most cases be built and “productized” by the big players in the space, who will then turn around and offer them to smaller financial institutions. Both parties benefit in this type of collaboration..
Do AI-based payment solutions require adoption of a platform-as-a-service approach?
VB: Definitely. PaaS solutions and partnerships are where the sharing of AI tools will happen. [PaaS essentially is a plug-and-play model that allows producers and consumers to connect and interact with each other, and exchange value. It’s what Amazon uses, and banks and credit unions can serve as such a platform as well.]
The need for PaaS is because the data flowing into AI-powered solutions is not owned by any single entity. It is a different approach from every financial institution needing its own enterprise resource planning (ERP) or customer relationship management (CRM) system, which are built for first-party data the institution owns. The data powering AI solutions is coming from many different channels and there is no way all of these systems can be built internally, so PaaS will be critical to AI adoption in financial services.
Could a data-powered combo like Apple Card/Goldman Sachs dominate in the U.S. as Alipay does in China?
JB: There is a high likelihood that there will be another major payments disruption by a company that has literally no background in banking or financial services. Apple has unprecedented access to consumer data that Goldman Sachs doesn’t. So, yes, a disruptor in the payments space with a company that grabs a huge part of the payments processing market is possible in the U.S. similar to the Alipay ecommerce and payment platform in China or the PayTM payment app in India.
Existing financial institutions should guard against this disruption by creating more innovative products, even partnering with traditional competitors to do so. If a retail financial institution can provide a consumer with all of the benefits of an ecommerce platform like Alipay, it would make it hard for the consumer to switch.